Adaptivity via a Parallel Architecture for Stochastic Gradient Methods
cs.LG, cs.AR, math.OC
Submitted: 2026-07-31
Updated: 2026-08-26
Terminology
Sources
- Distributed Stochastic Optimization via Adaptive SGD
- AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods
- MT-DAO: Multi-Timescale Distributed Adaptive Optimizers with Local Updates
- Adam: A Method for Stochastic Optimization
- Adaptive Bound Optimization for Online Convex Optimization
- ADADELTA: An Adaptive Learning Rate Method
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks